Whitepaper
Verified data in the AI era: The new trust layer for financial decisions
Underwriting, audit, compliance and eligibility decisions are accelerating with the use of machines, and the cost of an unverified input is rising right along with them.
This paper examines why documents alone can no longer carry the weight the industry has placed on them, what automation can do to the cost of a bad decision and what a source-verified architecture looks like once in practice.
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Inside the paper
AI is making financial decisions faster. It is also making the data behind those decisions easier to fake.
- The unpriced risk in financial automation
- Where misplaced trust enters the system
- From paper to digital: The limits of document-based verification
- The evidence: When document-level trust breaks down
- The economics of error: What changes once humans leave the loop
- 3 tests: How to discern what makes data trustworthy
- The architecture of a source-first system
- Verified data is the new trust layer beneath AI
Whitepaper
Verified data in the AI era: The new trust layer for financial decisions
Veriv.com
You wouldn’t let an intern set your credit risk model
Jake Gutting, Chief Product Officer, Veriv
3 tests
How to discern what makes data trustworthy
Three connected tests help determine whether information has a defensible basis for trust:
Provenance
Asks whether the data can be traced to its source, not merely to where it was last seen or handed off.
Authentication
Asks whether the party providing the data actually controlled the underlying record, or simply had a copy.
Tamper Resistance
Asks whether the record can be shown to have remained unaltered from where it left the authoritative source.